Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.

1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 9, No. 2, April 2019, pp. 982~991
ISSN: 2088-8708, DOI: 10.11591/ijece.v9i2.pp982-991  982
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Framework for progressive segmentation of chest radiograph
for efficient diagnosis of inert regions
Savitha S. K.1
, N. C. Naveen2
1
Department of CSE, Visvesvaraya Technological University, Belagavi, Karnataka, India
2
Department of Computer Science & Engineering, JSSATE, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Jun 30, 2018
Revised Oct 27, 2018
Accepted Nov 20, 2018
Segmentation is one of the most essential steps required to identify the inert
object in the chest x-ray. A review with the existing segmentation techniques
towards chest x-ray as well as other vital organs was performed. The main
objective was to find whether existing system offers accuracy at the cost of
recursive and complex operations. The proposed system contributes to
introduce a framework that can offer a good balance between computational
performance and segmentation performance. Given an input of chest x-ray,
the system offers progressive search for similar image on the basis of
similarity score with queried image. Region-based shape descriptor is applied
for extracting the feature exclusively for identifying the lung region from the
thoracic region followed by contour adjustment. The final segmentation
outcome shows accurate identification followed by segmentation of apical
and costophrenic region of lung. Comparative analysis proved that proposed
system offers better segmentation performance in contrast to existing system.
Keywords:
Chest radiograph
Chest x-ray
Disease detection
Lung
Segmentation
Copyright © 2019 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Savitha S. K.,
Department of CSE,
Visvesvaraya Technological University,
Belagavi, Karnataka, India.
Email: savithassk80@gmail.com
1. INTRODUCTION
Irrespective of presence of various forms of radiological evaluation, chest x-ray is one of the most
frequently prescribed by the doctor [1]. It is because chest x-ray is one of the most economical imaging
practices that reveal multiple set of clinical information about chest e.g. blood vessels, problems associated
with heart, possible fracture of rib cages, actual condition of lungs, outline of heart, any form of calcium
deposit, any abnormal changes after surgery, position of implantable devices, etc [2], [3]. However, a chest x-
ray also consists of various problems e.g. illumination, rotation of object, opacity, abnormality in contrast and
brightness, etc. Such problems acts as an impediment towards accurate diagnosis of any disease related to
chest. Hence, for an effective diagnosis, it is necessary that the focus of the radiologist / physician should be
only concentrated to identify the correct location of chest. This problem is effectively solved by segmentation
process [4]. Basically, when a chest radiograph is subjected to segmentation, it can be expected that the
outcome result should be splitting of the chest radiographs into its smaller component in the form of object or
region [5]. It basically assists in segregating unnecessary detailing (i.e. background) from the essential object
(foreground), so that the diagnosis can be carried out with better accuracy. Another benefit of carrying out
segmentation process in chest x-ray is that it assists in quantifying the tissues, identification of any oddities,
estimating volume of target object (e.g. malignant / benign masses), etc [6]. In a nutshell, the core objective
of the image segmentation methods is to construct a simpler representation of the targeted object or region
and therefore the outcome of segmentation process is never single unit. It is a set of component that could
collectively formed the actual image. In existing system, there are various authors that have carried out

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research work towards segmentation considering medical images e.g. [7]-[9]. However, complexities
associated with the chest x-ray are quite different compared to other medical images. At present times, there
are only two fixed positions for capturing chest x-ray i.e. posterior-anterior position and lateral position.
Although, these positions offers 90% visualization to maximum clinical problems associated with chest,
but sometime it veils visualization to some critical problems e.g. nodules, masses, lumps, lesions owing to
presence of bones (i.e. rib cases, clavicle bone, etc). Therefore, the bigger challenge in performing
segmentation is to find such occluded objects and extract the targeted object as foreground.
Due to complexity associated with performing segmentation of such inert region of the chest,
majority of the existing research work is found to adopt machine learning techniques or iterative techniques
on the basis of certain predefined information. Although, all these existing techniques are claimed to offer
good segmentation performance, but their computational performance is quite questionable owing to
involvement of higher degree of recursive operation. Therefore, the present manuscript introduces a simple
and cost effective framework that is capable of performing segmentation using progressive approach that has
acted as a better alternative of recursive approach in existing system. The proposed system also introduces an
analytical model to claim the segmentation performance. Section 2 discusses about the existing research
contribution followed by brief outlining of problems associated with existing system in Section 3. Adopted
research methodology of proposed system is briefed in Section 4 followed by elaborated discussion of
algorithm design in Section 5. Discussion of results accomplished in the proposed study is carried out in
Section 6 and finally the concluding remarks is done in Section 7.
2. RELATED WORK
This section briefs about the existing research techniques carried out towards segmentation
techniques in medical images. Researchers have reviewed about existing imaging techniques of chest
radiographs [10] and thereby extend the discussion in the line of chest radiograph explicitly. At present,
there are various ranges of techniques implemented for segmentation. The work carried out by Wang and
Guo [11] have presented a combined implementation of identifying skin boundary, segmenting contour
regions, and refinement of lung region. Most recently, a unique segmentation process of segregating heart
from the lung field was introduced by Dai et al. [12]. The authors have used convolution-based segmentation
technique in order to construct a network that can discretize between the ground truth information and
synthesized mask. Adoption of threshold-based segmentation scheme can be observed in the work of Shi
et al. [13]. The authors have used random walk algorithm in order to segment lung from chest region further
curvature-based technique was utilized to smoothen the contours. A non-conventional technique of vector
quantization has been found to assist in segmentation as well for chest radiographs as seen in the work of
Han et al. [14]. The work carried out by Shen et al. [15] has used a chain coding technique along with
supervised learning algorithm for carrying out lung segmentation focusing on accuracy. Similar direction of
emphasis towards accuracy in segmentation performance was also carried out by Chae et al. [16]. The author
contributed to present a technique for reconstructing region of segmentation thereby enhancing segmentation
performance.
Gill et al. [17] have presented an atlas-based model for carrying out segmentation using affine
transformation scheme. Ngo and Carneiro [18] have presented a deep learning mechanism using level set
method for lung segmentation. The technique presents good optimization towards shape features during
segmentation. Filho et al. [19] have implemented singularity-based technique integrated with region-growing
method and thresholding scheme together to perform lung segmentation. Ruiz et al. [20] have presented a
heart segmentation technique using thresholding based scheme, filtering, and morphological operations.
Farag et al. [21] have introduced a model that implements shape module along with statistical information
about the intensity. The technique also uses a density estimation method using non-parametric approach for
effective lung segmentation. Lassen et al. [22] have adopted a watershed algorithm for achieving better
classification of lung section during transformation process. However, the process involves lack of efficiency
in learning process prior to perform segmentation. Such problems of learning were addressed in the work of
Feulner et al. [23], [24] by using discriminative approach for segmenting lymph node. The authors have
utilized graph cut algorithm for carrying out segmentation. Jirapatnakul et al. [25] have used surface
estimation technique to identify and segment pulmonary masses from chest portion. Lo et al. [26] have used
region growing mechanism along with morphological operation in order to perform segmentation.
The technique also uses fuzzy logic as well as anatomical modeling using semantic features for enhancing the
segmentation performance. Usage of fuzzy theory has also been reported in the work of Zhou et al. [27] for
assisting in segmentation. The technique uses correlation of pixels in order to perform detection.
Fuzzy clustering technique was also reported to be used in the work of Ji et al. [28] for addressing
segmentation problem.

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Pu et al. [29], [30] have presented a unique tree-based scheme considering curvature of human
airway for assisting in threshold-based lung segmentation. The scheme also proved that adoption of radial-
basis function can significantly assist in identification of fissures over lung surfaces. Usage of neural network
and wavelets is reported to enhance the segmentation methods as discussed in work of Ceylan et al. [31].
The authors have also used region-growing method for performing segmentation. Shikata et al. [32] have
discussed a tree-based algorithm that works along with Eigen values for constructing terminal portions of the
tissues on lungs. Chen et al. [33] have presented a technique that uses energy function along with kernel for
assisting in segmentation. The complete modeling is carried out considering statistical approach. Adoption of
region growing technique has also been reported in the work carried out by Jiang et al. [34]. Thresholding-
based segmentation scheme was witnessed to offer better system performance during segmentation process
as seen in the work of Liu et al. [35].
There are various literatures towards segmentation problems that has also been considering different
medical case study apart from lungs. Enikov and Anton [36] have used machine learning technique for
segmenting images with cervical spines. Thresholding-based scheme was adopted by Wang et al. [37] for
faster process of medical image segmentation. Jiang et al. [38] have used level set method along with region
growing for performing segmentation of brain images. Incorporation of game theory for improving
segmentation was discussed in work of Zhong and Wu [39]. Integrated implementation of edge as well as
region was reported in the work of Luo et al. [40] where the segmentation performance has been optimized
using swarm intelligence. Benefits of multi-thresholding schemes for segmenting brain images was discussed
by Liu et al. [41]. Study towards automated segmentation is put forward by Mechrez et al. [42] considering
brain images. The authors have used a recursive patch of images in order to perform segmentation. Similar
brain images were also investigated for segmentation by Cong et al. [43] who used field estimation
technique. Akhavan and Faez [44] illustrated the segmentation algorithm for Retinal blood vessel image by
using Fuzzy and medial filter approach and found effective in detection of retinal blood vessels. A research
towards automatic medical image segmentation is found in Seada et al. [45] and named the model as
"ascending aorta". The model outcomes with nealy 95% of accuracy in segmentation. A Doubly truncated K-
mean clustering and Laplace mixture model is presented for image segmentation by Jyothirmayi et al. [46].
In this, different pixelled images were analyzed the for superiority of model in segmentation.
Hence, there are multiple techniques for assisting in segmentation of medical image in literatures.
The next section outlines the problems associated with existing system.
3. PROBLEM IDENTIFICATION
After reviewing the existing system of segmentation, following problems has been identified:
i) usage of thresholding, rule, model based, edge-based, pixel-wise classification, and region-based schemes
are frequently used for performing segmentation. The significant problems of all the adopted methods are its
assumptions being highly heuristic. Hence, they are not much applicable for performing scalable
segmentation performance and can be used only in preliminary stages. This problem is more high for rule-
based techniques. ii) usage of deformable model-based techniques suffers from significant limitations of
higher contrast and prominent occlusion caused by edges of rib cases, higher dependency of reference model,
and scattered solution from multiple internal attributes during segmentation, iii) usage of machine learning
approach also results in higher dependencies on training operation to be carried out on dataset, iv) lower
convergence speed performance, v) majority of the schemes perform segmentation based on pixel related
information that increases accuracy of segmentation at the cost of computational complexity.
Existing machine learning mechanism performs highly iterative operation that increases the execution time of
the segmentation process. Therefore, there is a need of process that performs less iterative operation and
more progressive segmentation steps that could offer a better balance between accuracy and computation
time. We put forward logic that an effective segmentation algorithm always demand a good equilibrium
between computational complexity and accuracy and thereby maintain an optimal scalability performance.
The next section discusses about the adopted research methodology in order to address such problems.
4. PROPOSED METHODOLOGY
The proposed study is an extension of previous work [47] where the emphasis was on incorporating
multi-level of image pre-processing of chest radiograph. This part of the study introduces a simple and yet
novel segmentation policy as showcased in Figure 1. Adopting analytical research methodology,
the proposed system takes the input of a queried image and is capable of extracting horizontal and vertical
pixel information associated with either intensity or projection. Adoption of this strategy ensures mitigating
any form of adverse effect of rotations in the chest radiographs. The proposed system also introduces usage

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of content-based image retrieval technique in order to narrow down the matching images. The narrowness of
the matched image is further boosted with an aid of similarity score between the queried image and dataset.
The outcome of this process results in set of 5 matching images arranged on the basis of their ranks.
The image with 1st rank is considered for further processing where an extraction of feature is carried out
using visual descriptor. Finally, a simple image registration process is implemented that could further
ascertain the mapping of input to target image. In the entire process, accuracy of the matched image is highly
ensured and therefore a contour-based adjustment is further carried out to eliminate any selection of
unnecessary boundary or region of the lung. Applying cubic spline interpolation further make the system
more enhanced enough to perform segmentation. The proposed system is also capable of performing
segmentation as following i) segmenting complete lung region, ii) segmenting apical region, and iii)
segmenting costophrenic region in order to ensure further application of the outcome with respect to clinical
inference of chest radiographs.
Queried
Image
JSRT
dataset
Selected
Analysis
Option
Sum of
intensity
Sum of
Projection
CBIR
Similarity
Score
Query Image and set of matched images with ranks
Feature
extraction
Visual
Descriptor
Image
registration
Contour
Adjustment
Interpolation Segmentation
Option-1
Option-2
Figure 1. Schematic representation of process flow of proposed segmentation
5. ALGORITHM DESIGN
The proposed system emphasizes on the incorporating a precise segmentation process that can
finally assists in disease diagnosis from the given chest radiographs. The mechanism assists in formulating a
better search condition by focusing on the entire process of making the input image ready for segmentation.
The proposed system consists of 5 sequential algorithms in order to perform an effective segmentation
different from existing system i.e. i) Algorithm for processing the input image, ii) Algorithm for content-
based image retrieval, iii) Algorithm for local feature selection, iv) Algorithm for image registration process,
and v) Algorithm for segmentation. This section discusses about all the algorithms in brief.
1. Algorithm for processing the input image
Input: I
Output: H1, H2
Start
1. init I
2. I=r(I)
3. Sr=∑I
4. 4.Sc=∑(I,2)
5. [H1 b1]=hist(Sr max(Sr))
6. [H2 b2]=hist(Sc, max(Sc))
End
The algorithm takes the input I (Input Image) that after processing results in H1 (Horizontal
Projection), H2 (vertical projection). The input image I is digitized followed by applying resizing operation r
(Line-2). The algorithm computes the summation of row wise elements (Line-3) as well as column wise

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elements (Line-4). Histogram is formed considering the row elements corresponding to Sr (Line-5) and
column section consists of all the maximum values of Sc (Line-6). This operation is essential in the proposed
system as it offers the complete segmentation process to be undertaken either on summation of horizontal-
vertical elements or on projection of horizontal-vertical elements. The projection operation assists in
mitigating any form of rotations in the input image using vertical and horizontal orthogonal projection.
This makes the input image free from any forms of rotational effect. The next stage of the algorithm is to
apply content-based image retrieval process as shown below:
2. Algorithm for content-based image retrieval
Input: ϕ
Output: Sro, Sco, β
Start
1. For i=1: ϕ
2. [(H10 b10) (H20 b20)]ϕ(m i), where a=1, 2, 3, 4
3. Sro=∑(a ϕ), a=5
4. Sco=∑((a ϕ), 2)
5. End
6. For n=1:p
7. ix1=b1==p(n), p1(n)=H10(ix1)
8. ix2=b10==p(n), p2(n)=H10(ix2)
9. End
10. For n=1:q
11. ix1=b2==q(n), q1(n)=H1(ix1)
12. ix2=b20==q(n), q2(n)=H20(ix2)
13. End
14. get rw=n/(n+m)
15. For (x y)=1:(n m)
16. pt=pt+√{p1(x).p2(x)}
17. qt=qt+√{q1(x).q2(x)}
18. End
19. β α.qt+(1- α).qt
20. (m_I m_m) ϕ (a ix(1)), where a=5 and 6
21. (Sro Sco)=∑(m_I)
22. obtain (Sco H10 b10 H20 b20) ϕ(a, ix(1)),where a=1-4
End
This algorithm takes the input of ϕ (database) that after processing yields to Sro (sum of horizontal
intensity), Sco (sum of vertical intensity), β (similarity measure). A specific image dataset ϕ is used where
the first and second row element corresponds to horizontal projection H10 and b10 while third and fourth
row elements correspond to vertical projection H20 and b20 respectively (Line-2). The algorithm also
computes the summation of row-wise (Sro) and column-wise (Sco) element respectively (Line-3 and Line-4).
However, the algorithm chooses to consider sum of horizontal and vertical intensity (i.e. Sro and Sco) only
upon selection of summation of horizontal and vertical elements. On the other hand, the algorithm selects
horizontal and vertical projects (i.e. b10, H10 and b20, H20) upon selection of horizontal-vertical projection.
The next part of the algorithm considers similarity coefficient p that computes the common elements between
the projections b1 and b10 (Line-6). Considering all the values of p, the algorithm checks the projection b1 of
input image to be matching with unit value of projection in the database. Only the horizontal projections H10
are considered for further processing i.e. p1 (Line-7 and Line-8).The similar operation is carried out for
vertical projection with similarity coefficient q (Line-10) for calculating q1 and q2 (Line-11 and Line-12).
The algorithm also computes the relative weight rw using mathematical expression shown in Line-14, where
the variable n and m corresponds to length of p and q. The next part of the algorithm calculates a temporary
variables pt (Line-16) and qt (Line-17) in order to compute a final similarity coefficient β using the
expression shown in Line-19. A sorting process in descending order is carried out for the obtained value of
similarity coefficient. A matrix m_I and m_m represents matched image and (ongoing) matching image
respectively (Line-20). Finally, summations of row-wise and column-wise elements are captured (i.e. Sro and
Sco) from matched image m_I (Line-21) followed by acquiring of sum of horizontal intensity (Sro), sum of
vertical intensity (Sco), horizontal projection (b10, H10), and vertical projection (b20, h20) as shown in
Line-22. Therefore, the good availability of projection-based information significantly assists to mitigate any

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forms of misalignment in the image. At the same time, the algorithm of content-based image retrieval results
in good number of most relevant images thereby reducing the effort of search towards
best fit image. The algorithm also makes use of similarity coefficient in order to confirm the
relevancy score of the queried image with that of the dataset. Upon selection of the best matched image,
the image is now ready to be extracted for its feature using the algorithm below:
3. Algorithm for local feature selection
Input: I
Output: im3
Start
1. [(im1 im2) (des1 des2) (loc1 loc2)]=σ(p1 p2)
2. For i=1: des1
3. [v. in]sort(ic(dp))
4. If (v(1)<dR.v(2))
5. m(i)=in(1)
6. Else
7. m(i)=0
8. End
9. im3p1||p2
End
This algorithm is responsible for extracting local features from the matched image obtained from
content-based image retrieval process. The algorithm design is based on visual descriptor of region-based
shape method. The algorithm takes the input of I (processed input image) that after processing yields im3
(image with local feature). The variables p1 and p2 represents I*255 and m_I*255 respectively.
The algorithm implements a function σ for performing the extraction of local features in the form of sets e.g.
(im1 im2) (des1 des2) (loc1 loc2) as shown in Line-1. The algorithm computes the vector of dot product dp
of first descriptor (des1) and transpose of second descriptor (Line-3). This operation is further followed by
sorting operation in order to obtain v and in maps (Line-3). The algorithm then checks of the v(1) is found
more than distance ratio dR multiplied with v(2) than the unit index is assigned to match (Line-5) or else zero
is assigned (Line-7). The distance ratio dR only keep matches in which the ratio of vector angles from the %
nearest to second nearest neighbour is less than dR. Both the images obtained i.e. p1 and p2 as the outcome.
The processed image is further subjected to registration process as shown below:
4. Algorithm for image registration process
Input: I, m_I
Output: Ireg, mw
Start
1. let I1 and I2 be I and m_I
2. [Ireg m2]μ1(I2 I1, ρ)
3. tformμ2(I2 I1, ρ)
4. mwγ(m_m, tform)
End
This algorithm is mainly responsible for performing image registration process which takes the
input of I (processed input image), m_I (Matched image) in order to generate the output of Ireg (registered
image), mw (contour adjusted image). The proposed system implements a function as a configuration
suitable for registering images using multimodal approach. This function formulates an optimizer as well as
metric configuration in order to register the intensity-based image. The variable ρ depicts a set of both
optimizer and a metric as a structure with explicit information about different optimizer properties for
obtaining convergence over a global maximum. The algorithm implements a function μ1 for registering an
image (Line-2) as well as function μ2 for estimating the geometric transformation require to stabilize the
moving image (Line-3). The next step of the algorithm is to perform transformation of matched image m_m
as per the geometric transformation exhibited by tform. Hence, the outcome mw (Line-4) can be considered
as a transformed image that has better adjustment of the contours of the lung regions. The next part of the

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algorithm continues for final step of segmentation process as shown below:
5. Algorithm for identifying upper and lower lung segment
Input: I, mw
Output: u, l
Start
1. yτ(I)
2. bw=λ(mw)
3. [(xs ys)]co(), where co=f(bw)
4. [xo1 yo1]Ω(y, var)
5. 5.( m1 m2){argmin(ys1 ys2) argmax(ys1 ys2)}
6. uI(nr)
7. lI(end-nr)
End
The algorithm considers the input as transformed image mw obtained from the prior algorithm
(Line-2) and uses a function λ for binarizing the image and thereby resultant image of bw is obtained (Line-
2). The next step is to obtain the binarized boundaries co of the processed image bw (Line-3). The system
than extracts the binarized boundaries with respect to explicit rows in order to obtain (xs1 ys1) and (xs2 ys2).
A filtering function τ is invoked on image I (Line-1) followed by applying recursive process Ω (Line-4)
considering different variables var. Finally, the segmented image is generated by conjoining (xs ys) and
(xo yo) points as follows: the algorithm extracts the minimum arguments of (ys1 ys2) and maximum
arguments of (ys1 ys2) in order to form m1 and m2 respectively (Line-5). The algorithm extracts number of
rows nr and number of columns nc from input image I and obtains the upper portion u (Line-6) and lower
portion l (Line-7). A closer look into the algorithm shows that proposed segmentation technique is quite
different from all conventional segmentation technique in following manner viz. i) it is progressive and not
iterative as seen in existing techniques in literature, ii) the segmentation operation is more into optimizing the
detection performance unless existing system that only focuses on detection, iii) the image registration
process followed by contour adjustment further fine-tunes the edges of lung region without even applying
conventional edge-based segmentation process. This causes the algorithm to obtain more accurate
information about lung region and performs faster identification of it. The next section outlines the result
obtained by proposed study.
6. RESULT ANALYSIS
The assessment of the proposed study has been carried out using Japanese Society Radiological
Technology (JSRT) dataset of chest radiograph where the size of the images is 8,192 KB. All the images are
gray-scale images with color depth of 12 bit. This size is quite bigger compared to conventional medical
images.
The study outcome of the proposed system has been compared with the existing segmentation
techniques. There are diverse studies in literatures pertaining to segmentation and hence we choose the one
that is frequently adopted in segmentation i.e. edge based, threshold-based, and region-based schemes.
Table 1 highlights the outcome of comparative analysis of proposed and existing system with respect to
Edge-based, threshold-based, and region-based.
Table 1. Outcome of Comparative Analysis
Performance Factors Existing [47] Proposed
Edge-based Threshold-based Region-based
Execution Time (s) 1.01968 1.0265 4.2841 0.5487
Accuracy (%) 97% 98% 94% 99%
The region-wise segmentation process results in generation of too much redundant information on
the basis of certain pre-defined factor. Therefore, execution time is more compared to others. Edge-based
being the most frequently adopted technique that essentially depends on either histogram or gradient-based
information that increases the accuracy compared to region-based. However, its performance is nearly similar
to threshold-based segmentation technique. The prime reason for enhanced accuracy of proposed system are
i) narrowing of search objects from dataset on the basis of similarity score, ii) usage of region-based shape

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descriptor for further ensuring the correct selection of lung region, iii) image registration process integrated
with contour adjustment to finally identify the lung region followed by spine interpolation, and iv) capability
to identify the apical and costophrenic regions. Moreover usage of features to perform segmentation further
reduces the search time for similar object causing reduction in execution time for proposed system.
7. CONCLUSION
The contribution of existing literatures towards segmentation process in chest x-ray has not yet
addressed the problem associated with the position of capturing the radiograph. The proposed study considers
certain set of problems e.g. i) conventional position of capturing chest x-ray limits the visibility of certain
inert object and may potential acts as impediment in segmentation process, ii) existing segmentation
techniques are more inclined in using iterative operation that offers accuracy at the cost of computational
complexity, iii) more frequently existing techniques e.g. rule based, machine-learning based, threshold-based,
region-based, edge-based doesn’t assist much in addressing the problem of progressive segmentation. A
simple and novel progressive segmentation process is introduced where emphasis was on reduction of
iterations towards segmentation process. The study outcome shows enhanced accuracy and reduced
execution time as compared to frequently used segmentation algorithms.
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BIOGRAPHIES OF AUTHORS
Savitha S. K., working as Assistant Professor in Bangalore Institute of Technology, Bengaluru,
Karnataka in Computer Science Department. She has finished her B.E from Bangalore University,
India. She has done her M.Tech from Kuvempu University, India. She is pursuing PhD from VTU,
Belagavi, India. Her Area of Interest is in Digital Image Processing, Database Management
System and Data Mining. She has pubslished 6 papers in national and internation journals and
Conferences. She has around 12 years of academic experience.
Dr. Naveen N. C., working as Professor, Department of Computer Science & Engineering,
JSSATE, Bengaluru, India. He has completed B.E from Bangalore University, India. M.E from
Bangalore University, India. He has completed his PhD from SRM University, Chennai, India. He
has around 16 years of acandemic experience and 3 years of industrial experience. He has
published 12 papers in national and internation journals and conferences.